Input Requirements for RNAmod: Technical Specifications for Multi-Modification Epitranscriptome Analysis

Input Requirements for RNAmod: Technical Specifications for Multi-Modification Epitranscriptome Analysis

A Comprehensive Guide with Workflow Visualizations

1. Core Input Specifications

A. Sample Preparation Requirements

1. RNA Integrity & Quantity

   • Input Material: PolyA+ RNA (≥50 ng) for mRNA-focused analysis; total RNA acceptable for rRNA/tRNA modifications 23.

   • Purity: OD_260/280 ≥1.8, RIN ≥7.0 (Agilent Bioanalyzer).

   • PolyA Tail Preservation: Critical for direct RNA sequencing (DRS); avoid fragmentation to maintain full-length transcripts 1.

2. Library Construction

   • Adapter Ligation: Use Oxford Nanopore’s SQK-RNA002 kit with RNA CS (Control Strand) for signal calibration.

   • Barcoding: Optional but recommended for multiplexed samples (e.g., 12-plex Nanopore barcodes) 2.

B. Sequencing Data Specifications

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2. Data Preprocessing & Input Formats

A. Raw Data Requirements

Critical Input Components:

• Event-level Signals: Extracted using tombo resquiggle, aligning raw current signals to reference genome 2.

• Feature Matrix: Per 5-mer current intensity (pA), dwell time, and standard deviation 34.

• Reference Genome: Must match sample species (e.g., GRCh38 for human, IRGSP-1.0 for rice) 2.

B. Migration Learning Inputs

For novel modification detection (e.g., m⁷G, Inosine):

1. Minimal Training Data:

   • ≥1,000 modified sites (e.g., from IVET datasets) 24.

2. Transfer Learning Protocol:

   • Freeze 1D-CNN/Bi-LSTM layers; retrain attention layers with new data.

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3. Quality Control Metrics

Pre-Analysis Checks:

Failure Impacts:

• Low RIN → degraded RNA → truncated reads → missed modifications.

• Poor alignment → erroneous feature extraction → false positives.

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4. Sample-Specific Considerations

A. Biological Matrices

B. Special Cases

• Low-Abundance Transcripts:

  • Increase coverage to ≥50X (e.g., oncogenes like BRCA1).

• FFPE Samples:

  • Not recommended; RNA fragmentation compromises full-length DRS.

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5. Workflow Integration & Output

Input-to-Output Pipeline

Output Specifications:

• BED Files: Single-base resolution modification calls (chromosome, position, modification type, confidence score).

• Visualization: Integrable with IGV for genome browser tracks 3.

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6. Advantages Over Conventional Methods

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Conclusion

RNAmod (exemplified by TandemMod) requires four critical inputs:

1. High-Quality RNA: Full-length polyA+ RNA with minimal degradation (RIN ≥7.0).

2. Nanopore DRS Data: FAST5 files from R10.4+ flow cells, basecalled with Guppy.

3. Event-Level Features: Current intensity, dwell time, and noise metrics per 5-mer.

4. Reference Genome: Species-specific genome for signal alignment.

This input framework enables simultaneous detection of m⁶A, m⁵C, Ψ, and other modifications at single-base resolution, outperforming antibody-based methods in throughput, cost, and multiplexing capability. The integration of transfer learning further reduces training data requirements by 60%, democratizing epitranscriptome analysis for diverse species and conditions—from cancer diagnostics to crop stress response studies.

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Data sourced from public references including:

1. Yuan et al., Nat Commun (2024): TandemMod technical validation 23

2. Nanopore Tech Guides: DRS library preparation (SQK-RNA002)

3. Genetics in Medicine Open (2025): Clinical RNA-seq integration 5

For academic collaboration or content inquiries: chuanchuan810@gmail.com